Pub Date : 2019-09-01DOI: 10.1109/VTCFall.2019.8891555
Li Zhu, Jiang Zhu, Shilian Wang, Li Hu, Qian Cheng
Hybrid beamforming in transmitter and receiver can improve the spectral efficiency of millimeterwave (mmWave) multiple-input multiple-output (MIMO) communication system significantly, and can reduce the complexity of communication system. However, when the number of transmitted data streams is large, the existing designing schemes of hybrid beamforming, such as sparse approximation by orthogonal matching pursuit(OMP) algorithm, suffer from performance degradation. In this paper, we propose dictionary learning (DL) algorithm to perform the design of hybrid beamforming for mmWave MIMO system. Simulation result shows that the spectral efficiency of hybrid beamforming based on DL approaches that of optimal beamforming without constraint, which is far superior to the spectral efficiency of hybrid beamforming based on OMP. Moreover, the convergence property of the proposed algorithm is also verified.
{"title":"Hybrid Beamforming Based on Dictionary Learning for Millimeter Wave MIMO System","authors":"Li Zhu, Jiang Zhu, Shilian Wang, Li Hu, Qian Cheng","doi":"10.1109/VTCFall.2019.8891555","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891555","url":null,"abstract":"Hybrid beamforming in transmitter and receiver can improve the spectral efficiency of millimeterwave (mmWave) multiple-input multiple-output (MIMO) communication system significantly, and can reduce the complexity of communication system. However, when the number of transmitted data streams is large, the existing designing schemes of hybrid beamforming, such as sparse approximation by orthogonal matching pursuit(OMP) algorithm, suffer from performance degradation. In this paper, we propose dictionary learning (DL) algorithm to perform the design of hybrid beamforming for mmWave MIMO system. Simulation result shows that the spectral efficiency of hybrid beamforming based on DL approaches that of optimal beamforming without constraint, which is far superior to the spectral efficiency of hybrid beamforming based on OMP. Moreover, the convergence property of the proposed algorithm is also verified.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85007702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/VTCFall.2019.8891457
Wei Gao, W. Peng, Jiajia Liu, Zhifeng Nie
With the evolution of wireless networks, new techniques including massive multiple-input multiple- output (MIMO) and millimeter wave are adopted to satisfy the demands for diversified services. However, it has been verified by field tests that the traditional wide sense stationary assumption for wireless channel does not hold anymore. As a result, traditional channel state information (CSI) acquisition methods, especially the statistical CSI acquisition, cannot be applied straightforwardly in such a circumstance. In this paper, we propose a pre-processing method for channel sensing in the non-stationary environment. Specifically, the data sampled from channel training is treated as a channel image, where the statistical channel state is represented by gray-scale. Then the computer vision technique, specifically, the edge detection method, is used on the channel image to detect the homogeneous sub-regions. Within each sub-region, the channel is statistically stationary, and then the CSI can be obtained by existing methods. It is verified by simulation results that, the proposed method can help to improve the CSI acquisition accuracy in the non- stationary environment.
{"title":"Computer Vision Based Pre-Processing for Channel Sensing in Non-Stationary Environment","authors":"Wei Gao, W. Peng, Jiajia Liu, Zhifeng Nie","doi":"10.1109/VTCFall.2019.8891457","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891457","url":null,"abstract":"With the evolution of wireless networks, new techniques including massive multiple-input multiple- output (MIMO) and millimeter wave are adopted to satisfy the demands for diversified services. However, it has been verified by field tests that the traditional wide sense stationary assumption for wireless channel does not hold anymore. As a result, traditional channel state information (CSI) acquisition methods, especially the statistical CSI acquisition, cannot be applied straightforwardly in such a circumstance. In this paper, we propose a pre-processing method for channel sensing in the non-stationary environment. Specifically, the data sampled from channel training is treated as a channel image, where the statistical channel state is represented by gray-scale. Then the computer vision technique, specifically, the edge detection method, is used on the channel image to detect the homogeneous sub-regions. Within each sub-region, the channel is statistically stationary, and then the CSI can be obtained by existing methods. It is verified by simulation results that, the proposed method can help to improve the CSI acquisition accuracy in the non- stationary environment.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77150430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/VTCFall.2019.8891245
Junge Zhu, Xi Huang, Yinxu Tang, Ziyu Shao
Equipped with specific IoT on-board devices, un- manned aerial vehicles (UAVs) can be orchestrated to assist in particular value-added service delivery with improved quality-of- service. Typically, services are delegated in the unit of tasks to a designated leader UAV, while the leader UAV splits each task into sub- tasks and offloads them to part of its nearby UAVs, a.k.a. helper UAVs, for timely processing. Such a decision making pro- cess, often referred to as UAV task offloading, still remains open and challenging to design, due to various uncertainties therein, such as the resource availability and instant workloads on helper UAVs. However, existing solutions often assume the knowledge of system dynamics is fully available and conduct decision making in an offline manner, resulting in excessive control overheads and scalability issues. In this paper, we study the UAV task offloading problem in an online setting and formulate it as a multi-armed bandits (MAB) problem with time-varying resource constraints. Then we propose VR-LATOS, a learning- aided offloading scheme that learns the unknown statistics from feedback signals while making effective offloading decisions in an online fashion. Results from both theoretical analysis and simulations demonstrate that VR-LATOS outperforms state-of-the-art schemes.
{"title":"Learning-Aided Online Task Offloading for UAVs-Aided IoT Systems","authors":"Junge Zhu, Xi Huang, Yinxu Tang, Ziyu Shao","doi":"10.1109/VTCFall.2019.8891245","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891245","url":null,"abstract":"Equipped with specific IoT on-board devices, un- manned aerial vehicles (UAVs) can be orchestrated to assist in particular value-added service delivery with improved quality-of- service. Typically, services are delegated in the unit of tasks to a designated leader UAV, while the leader UAV splits each task into sub- tasks and offloads them to part of its nearby UAVs, a.k.a. helper UAVs, for timely processing. Such a decision making pro- cess, often referred to as UAV task offloading, still remains open and challenging to design, due to various uncertainties therein, such as the resource availability and instant workloads on helper UAVs. However, existing solutions often assume the knowledge of system dynamics is fully available and conduct decision making in an offline manner, resulting in excessive control overheads and scalability issues. In this paper, we study the UAV task offloading problem in an online setting and formulate it as a multi-armed bandits (MAB) problem with time-varying resource constraints. Then we propose VR-LATOS, a learning- aided offloading scheme that learns the unknown statistics from feedback signals while making effective offloading decisions in an online fashion. Results from both theoretical analysis and simulations demonstrate that VR-LATOS outperforms state-of-the-art schemes.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85460086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/VTCFall.2019.8891126
L. Bentley, Joe MacInnes, Hannah Mason, R. Bhadani, T. Bose
A sliding window technique for vision-based autonomous navigation applications is a common approach to path mapping from extracted features. Mapping a path inside a captured image requires finding a series of waypoints representing the path. Previous approaches find these points by sliding a window along the path in fixed increments across one image dimension. After each slide, the center of the window in the other dimension is adjusted so that the window maximally covers the path in that area. These approaches, however, fails to map paths that experience sharp curvature since the windows slide along only one dimension. The method proposed herein uses a pseudo-derivative approach to sliding windows that improves upon the traditional technique by dynamically adjusting the windows along both image dimensions during each slide. In this method, the directional components of a vector representing the previous slide are used as a naive estimation to perform the current slide. If this fails to map the path, the vector direction is used to enlarge the window dimensions. The method was tested in the domain of autonomous vehicles for lane- following based on lane-markings. The algorithm proved to be successful with lanes possessing sharp curvature and discontinuities as compared to previous sliding window approaches.
{"title":"A Sliding Window for Path Mapping Based on a Pseudo-Derivative Method in Autonomous Navigation","authors":"L. Bentley, Joe MacInnes, Hannah Mason, R. Bhadani, T. Bose","doi":"10.1109/VTCFall.2019.8891126","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891126","url":null,"abstract":"A sliding window technique for vision-based autonomous navigation applications is a common approach to path mapping from extracted features. Mapping a path inside a captured image requires finding a series of waypoints representing the path. Previous approaches find these points by sliding a window along the path in fixed increments across one image dimension. After each slide, the center of the window in the other dimension is adjusted so that the window maximally covers the path in that area. These approaches, however, fails to map paths that experience sharp curvature since the windows slide along only one dimension. The method proposed herein uses a pseudo-derivative approach to sliding windows that improves upon the traditional technique by dynamically adjusting the windows along both image dimensions during each slide. In this method, the directional components of a vector representing the previous slide are used as a naive estimation to perform the current slide. If this fails to map the path, the vector direction is used to enlarge the window dimensions. The method was tested in the domain of autonomous vehicles for lane- following based on lane-markings. The algorithm proved to be successful with lanes possessing sharp curvature and discontinuities as compared to previous sliding window approaches.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85549260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/VTCFall.2019.8891420
Chia-Hung Lin, Yu-Chien Lin, Yue Bai, W. Chung, Ta-Sung Lee, H. Huttunen
The well-known cell-averaging constant false alarm rate (CA-CFAR) scheme and its variants suffer from masking effect in multi-target scenarios. Although order-statistic CFAR (OS-CFAR) scheme performs well in such scenarios, it is compromised with high computational complexity. To handle masking effects with a lower computational cost, in this paper, we propose a deep-learning based CFAR (DL- CFAR) scheme. DL-CFAR is the first attempt to improve the noise estimation process in CFAR based on deep learning. Simulation results demonstrate that DL-CFAR outperforms conventional CFAR schemes in the presence of masking effects. Furthermore, it can outperform conventional CFAR schemes significantly under various signal-to-noise ratio conditions. We hope that this work will encourage other researchers to introduce advanced machine learning technique into the field of target detection.
{"title":"DL-CFAR: A Novel CFAR Target Detection Method Based on Deep Learning","authors":"Chia-Hung Lin, Yu-Chien Lin, Yue Bai, W. Chung, Ta-Sung Lee, H. Huttunen","doi":"10.1109/VTCFall.2019.8891420","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891420","url":null,"abstract":"The well-known cell-averaging constant false alarm rate (CA-CFAR) scheme and its variants suffer from masking effect in multi-target scenarios. Although order-statistic CFAR (OS-CFAR) scheme performs well in such scenarios, it is compromised with high computational complexity. To handle masking effects with a lower computational cost, in this paper, we propose a deep-learning based CFAR (DL- CFAR) scheme. DL-CFAR is the first attempt to improve the noise estimation process in CFAR based on deep learning. Simulation results demonstrate that DL-CFAR outperforms conventional CFAR schemes in the presence of masking effects. Furthermore, it can outperform conventional CFAR schemes significantly under various signal-to-noise ratio conditions. We hope that this work will encourage other researchers to introduce advanced machine learning technique into the field of target detection.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85553101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/VTCFall.2019.8891569
P. Bhat, Joseph Oncken, Rajeshwar Yadav, Bo Chen, M. Shahbakhti, D. Robinette
The advancement in vehicle-to-vehicle and vehicle- to-infrastructure technologies makes it possible for vehicles to obtain the real-time information related to transportation and traffic infrastructure. This paper presents the development of an optimal velocity generation algorithm that leverages the availability of traffic and road information. The objective of this optimization problem is to generate a velocity trajectory within a prediction horizon to reduce tractive force while monitoring the overall travel time required for the trip. The developed algorithm reduces energy consumption by avoiding wasteful driving maneuvers and utilizes the opportunities to recuperate kinetic energy with regenerative braking capability. This non-linear constrained optimization algorithm is implemented by an automatic control and dynamic optimization (ACADO) toolkit for real-time execution. The energy reduction is observed in the evaluation results obtained with a vehicle model for the 2nd generation of GM Chevrolet Volt, developed at Michigan Technological University. An experimentally validated vehicle dynamic model is used for the assessment of energy consumption and vehicle performance.
{"title":"Generation of Optimal Velocity Trajectory for Real-Time Predictive Control of a Multi-Mode PHEV","authors":"P. Bhat, Joseph Oncken, Rajeshwar Yadav, Bo Chen, M. Shahbakhti, D. Robinette","doi":"10.1109/VTCFall.2019.8891569","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891569","url":null,"abstract":"The advancement in vehicle-to-vehicle and vehicle- to-infrastructure technologies makes it possible for vehicles to obtain the real-time information related to transportation and traffic infrastructure. This paper presents the development of an optimal velocity generation algorithm that leverages the availability of traffic and road information. The objective of this optimization problem is to generate a velocity trajectory within a prediction horizon to reduce tractive force while monitoring the overall travel time required for the trip. The developed algorithm reduces energy consumption by avoiding wasteful driving maneuvers and utilizes the opportunities to recuperate kinetic energy with regenerative braking capability. This non-linear constrained optimization algorithm is implemented by an automatic control and dynamic optimization (ACADO) toolkit for real-time execution. The energy reduction is observed in the evaluation results obtained with a vehicle model for the 2nd generation of GM Chevrolet Volt, developed at Michigan Technological University. An experimentally validated vehicle dynamic model is used for the assessment of energy consumption and vehicle performance.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85693726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/VTCFall.2019.8891251
Rahul Gupta, Sushant Kumar, S. Majhi
This paper proposes a blind modulation classification (MC) algorithm for linearly modulated signals of orthogonal frequency division multiplexing (OFDM) system. The proposed MC algorithm works with unknown frequency, timing, and phase offsets and without the prior requirement of channel statistics. In this research, a larger pool of modulation formats, i.e., binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK (OQPSK), minimum shift keying (MSK), and 16-quadrature amplitude modulation (16-QAM) for OFDM signal has been classified. Classification takes place in two stages. First, we compute the discrete Fourier transform (DFT) of the received OFDM signal and then a normalized fourth-order cumulant is used in frequency domain to classify OQPSK, MSK, and 16-QAM modulation formats. At the second stage, the normalized fourth-order cumulant is used on the DFT of the square of the received OFDM signal to classify BPSK and QPSK modulation formats. The success rate and computation of the proposed MC algorithm are evaluated and compared with the previous methods.
{"title":"Blind Modulation Classification for OFDM in the Presence of Timing, Frequency, and Phase Offsets","authors":"Rahul Gupta, Sushant Kumar, S. Majhi","doi":"10.1109/VTCFall.2019.8891251","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891251","url":null,"abstract":"This paper proposes a blind modulation classification (MC) algorithm for linearly modulated signals of orthogonal frequency division multiplexing (OFDM) system. The proposed MC algorithm works with unknown frequency, timing, and phase offsets and without the prior requirement of channel statistics. In this research, a larger pool of modulation formats, i.e., binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK (OQPSK), minimum shift keying (MSK), and 16-quadrature amplitude modulation (16-QAM) for OFDM signal has been classified. Classification takes place in two stages. First, we compute the discrete Fourier transform (DFT) of the received OFDM signal and then a normalized fourth-order cumulant is used in frequency domain to classify OQPSK, MSK, and 16-QAM modulation formats. At the second stage, the normalized fourth-order cumulant is used on the DFT of the square of the received OFDM signal to classify BPSK and QPSK modulation formats. The success rate and computation of the proposed MC algorithm are evaluated and compared with the previous methods.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84093241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/VTCFall.2019.8891431
Letizia Moro, H. Mehrpouyan
Localization in indoor environments is essential to further support automation in many scenarios such as warehouses and factories. Moreover, direction-of-arrival knowledge is essential to supporting high speed millimeter-wave (mmWave) links in indoor environments, since most mmWave links are of a line-of-sight nature to combat the high pathloss in this band. Accurate localization in indoor environments, however, has proved a challenging task due to multi- path fading methods such as trilateration alone do not result in accurate localization. As such, in this paper we propose to combine the knowledge of wireless localization methods with other sensors used for odometry to track the location of a mobile robot. This paper presents significant real world localization measurement results for both Wi-Fi and odometry in diverse environments at the Boise State University campus. Using these results, we devise an algorithm to combine data from both odometry and wireless localization. This algorithm is shown in hardware testing to enhance the localization accuracy by reducing the localization error for a mobile robot.
{"title":"Hybrid Localization: A Low Cost, Low Complexity Approach Based on Wi-Fi and Odometry","authors":"Letizia Moro, H. Mehrpouyan","doi":"10.1109/VTCFall.2019.8891431","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891431","url":null,"abstract":"Localization in indoor environments is essential to further support automation in many scenarios such as warehouses and factories. Moreover, direction-of-arrival knowledge is essential to supporting high speed millimeter-wave (mmWave) links in indoor environments, since most mmWave links are of a line-of-sight nature to combat the high pathloss in this band. Accurate localization in indoor environments, however, has proved a challenging task due to multi- path fading methods such as trilateration alone do not result in accurate localization. As such, in this paper we propose to combine the knowledge of wireless localization methods with other sensors used for odometry to track the location of a mobile robot. This paper presents significant real world localization measurement results for both Wi-Fi and odometry in diverse environments at the Boise State University campus. Using these results, we devise an algorithm to combine data from both odometry and wireless localization. This algorithm is shown in hardware testing to enhance the localization accuracy by reducing the localization error for a mobile robot.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72790413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/VTCFall.2019.8891149
Amr S. El-Wakeel, A. Noureldin, N. Zorba, H. Hassanein
Future smart cities are profoundly looking forward to providing services that assure daily competent functionality. Efficient traffic management and related vehicular services are crucial aspects when considering the city’s decent operation. The significant presence of the vehicular and smartphone sensing and computing capabilities within and amongst the vehicles open the door towards robust vehicular and road services. The retrofitted present and future vehicles will be able to provide accurate real-time information about the road conditions and hazards, driver behaviour, and traffic. Adequate geo-referencing is remarkably demanded in order to preserve robustness while providing vehicular services. Present and widely spread global positioning systems (GPS) receivers are providing low- resolution position update at 1 Hz, which is not sufficient at high speeds. Also, alternative high data rate geo-referencing technologies may face self-contained or environmental-based performance limitations. In this paper, we propose an adaptive resolution integrated geo-referencing framework that augments GPS and inertial sensors to provide accurate localization and positioning for road information services. Also, we examine the effectiveness of the proposed system in geo- referencing for selected real-life road services.
{"title":"A Framework for Adaptive Resolution Geo-Referencing in Intelligent Vehicular Services","authors":"Amr S. El-Wakeel, A. Noureldin, N. Zorba, H. Hassanein","doi":"10.1109/VTCFall.2019.8891149","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891149","url":null,"abstract":"Future smart cities are profoundly looking forward to providing services that assure daily competent functionality. Efficient traffic management and related vehicular services are crucial aspects when considering the city’s decent operation. The significant presence of the vehicular and smartphone sensing and computing capabilities within and amongst the vehicles open the door towards robust vehicular and road services. The retrofitted present and future vehicles will be able to provide accurate real-time information about the road conditions and hazards, driver behaviour, and traffic. Adequate geo-referencing is remarkably demanded in order to preserve robustness while providing vehicular services. Present and widely spread global positioning systems (GPS) receivers are providing low- resolution position update at 1 Hz, which is not sufficient at high speeds. Also, alternative high data rate geo-referencing technologies may face self-contained or environmental-based performance limitations. In this paper, we propose an adaptive resolution integrated geo-referencing framework that augments GPS and inertial sensors to provide accurate localization and positioning for road information services. Also, we examine the effectiveness of the proposed system in geo- referencing for selected real-life road services.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72882511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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